Modern speaker embeddings are computed from neural models trained to classify speaker identities from a large pool of speakers [1]}, [2]}, [3]}.
A temporal statistics pooling layer is used to map the variable length input to a fixed-length representation.
After training, the fixed-length speaker embeddings are extracted from the activations of the penultimate layer in the network.
